Longitudinal processes often pose nonlinear change patterns. Latent basis growth models (LBGMs) provide a versatile solution without requiring specific functional forms. Building on the LBGM specification for unequally-spaced waves and individual occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. This provides a unified approach to nonlinear, interconnected trajectories. Simulation studies demonstrate that the proposed model can provide unbiased and accurate estimates with target coverage probabilities for the parameters of interest. Real-world analyses of reading and mathematics scores demonstrates its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.

翻译：纵向过程常呈现非线性变化模式。潜在基础增长模型提供了一种无需特定函数形式的通用解决方案。基于Liu与Perera（2023）针对不等间隔测量波次及个体时机提出的LBGM规范，我们将潜在基础增长模型扩展至多元纵向结果变量，由此建立了一种统一方法用于分析非线性、相互关联的发展轨迹。模拟研究表明，所提出的模型能够为目标参数提供无偏且精确的估计，并达到目标覆盖率。基于阅读与数学成绩的真实数据分析展示了该模型在分析具有不同时间模式的联合发展过程中的有效性。文中附有计算代码。